Improving SSVEP-BCI performance using pre-trial normalization methods

James Henshaw, Wei Liu, Daniela M. Romano

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

A brain-computer interface (BCI) enables users to communicate through a computer using only their brain signals, by extracting brain signal features containing information representative of the user's intent, and can be used in a wide variety of areas such as entertainment, rehabilitation, or assistive technologies. In this paper, two novel normalization methods are assessed with the aim of improving the quality of the extracted features: Baseline-Corrected canonical correlation analysis (BC-CCA), and Scaled CCA. Both methods are found to be able to improve classification accuracy in conditions using frequencies with a large range, whilst BC-CCA is the superior of the two, improving SSVEP detection accuracy by as much as 9.22%.

Original languageEnglish
Title of host publication8th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538612644
DOIs
Publication statusPublished - Sept 2017
Event8th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2017 - Debrecen, Hungary
Duration: 11 Sept 201714 Sept 2017

Publication series

Name8th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2017 - Proceedings
Volume2018-January

Conference

Conference8th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2017
Country/TerritoryHungary
CityDebrecen
Period11/09/1714/09/17

Keywords

  • BCI
  • brain-computer interface
  • EEG
  • normalization
  • SSVEP

ASJC Scopus subject areas

  • Communication
  • Cognitive Neuroscience
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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